1. Automated Anomaly Detection for Cloudflare with Datadog Integration


    To set up an automated anomaly detection system for Cloudflare using Datadog integration, you'll need to utilize the Cloudflare and Datadog providers to configure the resources appropriately. This might include setting up Cloudflare monitoring log push jobs or security rules and feeding them into Datadog metrics or security monitors.

    Pulumi doesn't provide a Datadog provider out of the box, but you can use the Pulumi Cloudflare provider to establish the Cloudflare side of things and set up a skeleton for later integration with Datadog using their API or an official integration method provided by Datadog.

    Firstly, you'd set up Cloudflare resources like cloudflare.Filter, cloudflare.Ruleset, or cloudflare.Argo that can help you in monitoring traffic patterns or logs. Then, you would typically set up a Datadog monitor that uses the information from these logs or patterns for anomaly detection.

    Since an integration with Datadog would likely involve using the Datadog API, you would need to handle this part outside of Pulumi in your application or scripts that use Datadog's API to create and manage monitors for anomaly detection based on the Cloudflare data.

    Below is a basic Python program using Pulumi that sets up Cloudflare filters and rulesets. This lays the groundwork for the data you would monitor in Datadog:

    import pulumi import pulumi_cloudflare as cloudflare # Get this from your Cloudflare dashboard or configure using pulumi config set cloudflare_zone_id = "your-cloudflare-zone-id" # Example Cloudflare filter that matches HTTP requests with a status code of 400 or greater cloudflare_filter = cloudflare.Filter("badRequestsFilter", zone_id=cloudflare_zone_id, expression="(http.request.uri.path ~ \"/*\") and (http.response.status_code >= 400)", paused=False, description="Filter for bad HTTP requests") # Example Cloudflare ruleset that uses the previous filter to block requests or log them cloudflare_ruleset = cloudflare.Ruleset("badRequestsRuleset", zone_id=cloudflare_zone_id, kind="zone", phase="http_request_firewall_custom", description="Ruleset for blocking bad requests", rules=[cloudflare.RulesetRuleArgs( action="block", expression=cloudflare_filter.expression, description="Block bad HTTP requests" )] ) # Export the ID of the filter and the ruleset pulumi.export("filter_id", cloudflare_filter.id) pulumi.export("ruleset_id", cloudflare_ruleset.id)

    This Pulumi program is a starting point, and to create an automated anomaly detection system:

    1. You create a Cloudflare filter (cloudflare.Filter) that specifies an expression to match web requests based on criteria indicating an anomaly.
    2. You create a Cloudflare ruleset (cloudflare.Ruleset) that uses the filter and defines an action (e.g., block, challenge, log) based on the criteria.
    3. You would then connect to the Datadog API using the output from these Cloudflare logs and create or update Datadog monitors to perform anomaly detection.

    Remember that you'd need to replace "your-cloudflare-zone-id" with your actual Cloudflare Zone ID.

    Additionally, since integrating Cloudflare with Datadog for anomaly detection requires setting up Datadog monitors through their API, you might need an HTTP client, SDK, or tool like curl along with the appropriate API credentials for Datadog to accomplish this. Datadog's documentation provides information on setting up API access, creating API keys, and making requests to their API for monitoring and alerting purposes.

    It's important to note that while this Pulumi program sets up the Cloudflare side, the full automated anomaly detection system will only be complete once you've implemented the connection to Datadog and configured the respective monitors.